In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import mpl_toolkits as mplot3d
In [2]:
# DF1 = Total and facet scores, and score per question
df1 = pd.read_excel('Survey Data.xlsx', sheet_name = 'Risk Facet scores')
df1
Out[2]:
responseid q8_2_1 q8_2_2 q8_2_3 q8_2_4 q8_2_5 q8_3 q8_4 q8_5berlin_1 q8_5london_1 ... Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
0 6873 1 1 1 1 2 2 4 1 1 ... 2 1 1 6 20 50 40 33.333333 25 31.578947
1 14129 2 2 2 1 2 4 3 1 1 ... 3 1 2 10 80 0 60 33.333333 50 52.631579
2 5861 1 1 1 2 2 4 4 2 1 ... 3 1 1 8 40 50 60 33.333333 25 42.105263
3 13364 2 1 2 1 1 4 1 2 1 ... 3 0 0 5 40 0 60 0.000000 0 26.315789
4 14112 2 2 2 2 2 2 3 2 1 ... 3 2 3 13 100 0 60 66.666667 75 68.421053
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
647 14135 1 1 2 1 2 4 3 2 1 ... 5 3 3 13 40 0 100 100.000000 75 68.421053
648 14155 2 2 2 2 2 4 4 2 1 ... 4 1 3 14 100 50 80 33.333333 75 73.684211
649 14274 1 1 1 2 2 3 4 2 1 ... 4 3 3 13 40 50 80 100.000000 75 68.421053
650 14448 2 2 2 2 2 2 2 2 1 ... 2 1 0 8 100 0 40 33.333333 0 42.105263
651 15717 1 2 1 1 2 4 4 2 1 ... 3 3 3 12 40 50 60 100.000000 75 63.157895

652 rows × 32 columns

In [3]:
# DF2 = Facet scores and total score, with Income, wealth and ISCED

df2 = pd.read_excel('Survey Data.xlsx', sheet_name = 'MAIN')
df2
Out[3]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
0 6873 6 NaN 1 25 2 1 1 2 1 1 6 20 50 40 33.333333 25 31.578947
1 14129 1 3000.0 2 30 2 4 0 3 1 2 10 80 0 60 33.333333 50 52.631579
2 5861 2 0.0 1 43 3 2 1 3 1 1 8 40 50 60 33.333333 25 42.105263
3 13364 5 105000.0 1 32 2 2 0 3 0 0 5 40 0 60 0.000000 0 26.315789
4 14112 3 34865.0 2 32 2 5 0 3 2 3 13 100 0 60 66.666667 75 68.421053
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
647 14135 1 500.0 2 33 2 2 0 5 3 3 13 40 0 100 100.000000 75 68.421053
648 14155 6 NaN 2 29 2 5 1 4 1 3 14 100 50 80 33.333333 75 73.684211
649 14274 2 NaN 2 74 4 2 1 4 3 3 13 40 50 80 100.000000 75 68.421053
650 14448 3 NaN 2 31 2 5 0 2 1 0 8 100 0 40 33.333333 0 42.105263
651 15717 1 NaN 2 26 2 2 1 3 3 3 12 40 50 60 100.000000 75 63.157895

652 rows × 18 columns

In [4]:
# The following graph represents frequency of each data point on "Total Score_19" or the total score out of 19 questions across the sample.

df2['Total Score_19'].plot(kind = 'hist', xticks = np.arange(0, 20, step=1), xlabel = 'Total Score_19', title = 'Frequency of Scores' )
Out[4]:
<Axes: title={'center': 'Frequency of Scores'}, xlabel='Total Score_19', ylabel='Frequency'>
No description has been provided for this image
In [5]:
# Absolute mean scores for each facet

(df2[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4','Total Score_19']].mean(axis = 0)).plot(kind = 'bar', title = 'Absolute mean of scores for above data set')
Out[5]:
<Axes: title={'center': 'Absolute mean of scores for above data set'}>
No description has been provided for this image
In [6]:
# Normalised mean scores for each facet

df2[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean(axis = 0).plot(kind = 'bar', title = 'Normalised mean of scores for above data set')
Out[6]:
<Axes: title={'center': 'Normalised mean of scores for above data set'}>
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [7]:
# DF3 = Sorted by income

df3 = df2.sort_values(by = 'Q12. Income')
df3 = df3.reset_index(drop = True)
df3.drop(df3[df3['Q12. Income'] == 7].index, inplace = True)

df3
Out[7]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
0 15717 1 NaN 2 26 2 2 1 3 3 3 12 40 50 60 100.000000 75 63.157895
1 219 1 80000.0 2 47 3 5 1 4 2 3 15 100 50 80 66.666667 75 78.947368
2 845 1 NaN 3 50 3 1 0 2 1 1 5 20 0 40 33.333333 25 26.315789
3 8599 1 2500.0 3 42 3 3 1 3 3 1 11 60 50 60 100.000000 25 57.894737
4 12715 1 200.0 2 31 2 3 0 2 0 2 7 60 0 40 0.000000 50 36.842105
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
572 7893 6 1000.0 1 41 3 1 1 3 1 4 10 20 50 60 33.333333 100 52.631579
573 6296 6 NaN 1 41 3 5 1 4 1 3 14 100 50 80 33.333333 75 73.684211
574 5806 6 NaN 1 40 3 2 1 3 2 3 11 40 50 60 66.666667 75 57.894737
575 1086 6 NaN 1 43 3 3 1 3 3 1 11 60 50 60 100.000000 25 57.894737
576 679 6 NaN 3 40 3 2 0 1 1 1 5 40 0 20 33.333333 25 26.315789

577 rows × 18 columns

In [8]:
# Absolute mean score for each facet (Sort by Income)

df3[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4','Total Score_19']].mean(axis = 0).plot(kind = 'bar', title = 'Absolute mean score for each facet (Sort by Income)')
Out[8]:
<Axes: title={'center': 'Absolute mean score for each facet (Sort by Income)'}>
No description has been provided for this image
In [9]:
# Normalised mean score for each facet (Sort by Income)

(df3[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean(axis = 0)).plot(kind = 'bar', title = 'Normalised mean score for each facet (Sort by Income)')
Out[9]:
<Axes: title={'center': 'Normalised mean score for each facet (Sort by Income)'}>
No description has been provided for this image
In [10]:
# Normalised mean of each facet as a numerical (sorted by income)

df3.groupby('Q12. Income')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%']].mean()
Out[10]:
Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_%
Q12. Income
1 48.888889 38.333333 58.000000 60.740741 45.555556
2 57.217391 41.739130 63.304348 61.739130 50.869565
3 58.181818 42.045455 58.787879 63.131313 50.189394
4 62.886598 46.391753 63.711340 62.886598 55.154639
5 52.876712 36.301370 60.547945 57.990868 50.000000
6 64.857143 48.571429 69.142857 67.619048 59.642857
In [11]:
# Count of responses for each category (sorted by income)

df3.groupby('Q12. Income')[['responseid']].count()
Out[11]:
responseid
Q12. Income
1 90
2 115
3 132
4 97
5 73
6 70
In [12]:
# Absolute mean of Total Facet score line plot (sorted by income)

df3.groupby('Q12. Income')[['Total Score_19']].mean().plot( kind = 'line', title = 'Absolute mean of Total Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[12]:
<matplotlib.legend.Legend at 0x235196c3e10>
No description has been provided for this image
In [13]:
# Absolute mean of each Facet score line plot (sorted by income)

df3.groupby('Q12. Income')[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[13]:
<matplotlib.legend.Legend at 0x23519843690>
No description has been provided for this image
In [14]:
# Normalised mean of each Facet score line plot (sorted by income)


df3.groupby('Q12. Income')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().plot( kind = 'line', title = 'Normalised mean of each Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[14]:
<matplotlib.legend.Legend at 0x23519878b50>
No description has been provided for this image
In [15]:
# Normalised mean of each Facet score stacked plot (sorted by income)

df3.groupby('Q12. Income')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%']].mean().plot( kind = 'bar', title = 'Normalised mean of each Facet score stacked plot (sorted by income)', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[15]:
<matplotlib.legend.Legend at 0x235198f3e10>
No description has been provided for this image
In [16]:
# Normalised mean of each Facet score hist plot (sorted by income)

df3.groupby('Q12. Income')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[16]:
<matplotlib.legend.Legend at 0x2351a1566d0>
No description has been provided for this image
In [17]:
# Trend line for Absolute Total Facet Score vs Q12. Income

sns.regplot (data = df3, x = 'Q12. Income', y = 'Total Score_19')
Out[17]:
<Axes: xlabel='Q12. Income', ylabel='Total Score_19'>
No description has been provided for this image
In [18]:
# Trend line for Absolute Independent Facet Score vs Q12. Income reponses

fig, ax = plt.subplots(figsize=(6, 6))

sns.regplot (data = df3, x = 'Q12. Income', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax, label='Certainty score_5')
sns.regplot (data = df3, x = 'Q12. Income', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax, label='Uncertainty score_2')
sns.regplot (data = df3, x = 'Q12. Income', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax, label='Number Comprehension score_5')
sns.regplot (data = df3, x = 'Q12. Income', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax, label='Graph Comprehension score_3')
sns.regplot (data = df3, x = 'Q12. Income', y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax, label='Calculation score_4' )

ax.set(ylabel='Scores', xlabel='Q12. Income')
ax.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [19]:
# Violine Plot for Total Score_19 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Total Score_19')
Out[19]:
<Axes: xlabel='Q12. Income', ylabel='Total Score_19'>
No description has been provided for this image
In [20]:
# Violine Plot for Total Score_19 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Calculation score_4')
Out[20]:
<Axes: xlabel='Q12. Income', ylabel='Calculation score_4'>
No description has been provided for this image
In [21]:
# Violine Plot for Graph Comprehension score_3 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Graph Comprehension score_3')
Out[21]:
<Axes: xlabel='Q12. Income', ylabel='Graph Comprehension score_3'>
No description has been provided for this image
In [22]:
# Violine Plot for Number Comprehension score_5 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Number Comprehension score_5')
Out[22]:
<Axes: xlabel='Q12. Income', ylabel='Number Comprehension score_5'>
No description has been provided for this image
In [23]:
# Violine Plot for Uncertainty score_2 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Uncertainty score_2')
Out[23]:
<Axes: xlabel='Q12. Income', ylabel='Uncertainty score_2'>
No description has been provided for this image
In [24]:
# Violine Plot for Certainty score_5 for each entry under Wealth.

sns.violinplot( data = df3, x = 'Q12. Income', y = 'Certainty score_5')
Out[24]:
<Axes: xlabel='Q12. Income', ylabel='Certainty score_5'>
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [25]:
# Data Frame 2 or df2 is an aggregate data on facet total scores and over all total score, along with wealth and income data vs each response. 
# This data set is also sorted by wealth with all responses 'NA' removed.
# We also assign quartiles ranking each response according to this sort.

df4 = df2.sort_values(by = 'Q14. Wealth')
df4 = df4.reset_index(drop = True)
df4 = df4.dropna(axis = 0, subset = 'Q14. Wealth')
df4.insert(loc = len(df4.columns), column = "Quartile Number", value = pd.qcut(df4["Q14. Wealth"],q = 4, labels = False ) + 1, allow_duplicates = 'False')

df4
Out[25]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_% Quartile Number
0 2305 4 0.0 1 29 2 1 0 3 2 2 8 20 0 60 66.666667 50 42.105263 1
1 5760 3 0.0 2 57 3 1 0 3 3 1 8 20 0 60 100.000000 25 42.105263 1
2 5463 6 0.0 1 30 2 2 1 4 3 2 12 40 50 80 100.000000 50 63.157895 1
3 796 2 0.0 1 35 2 5 2 4 2 3 16 100 100 80 66.666667 75 84.210526 1
4 2578 3 0.0 3 33 2 1 0 2 2 0 5 20 0 40 66.666667 0 26.315789 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
375 15 6 1250000.0 2 68 4 2 1 4 3 3 13 40 50 80 100.000000 75 68.421053 4
376 970 6 1350000.0 3 64 4 2 1 4 3 4 14 40 50 80 100.000000 100 73.684211 4
377 5592 7 1500000.0 2 64 4 3 1 5 3 3 15 60 50 100 100.000000 75 78.947368 4
378 10809 6 1650000.0 1 59 3 3 2 5 3 2 15 60 100 100 100.000000 50 78.947368 4
379 278 6 3500000.0 1 66 4 5 2 3 3 4 17 100 100 60 100.000000 100 89.473684 4

380 rows × 19 columns

In [26]:
# Absolute mean score for each facet (Sort by Wealth)

df4[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4','Total Score_19']].mean(axis = 0).plot(kind = 'bar', title = 'Absolute mean score for each facet (Sort by Wealth)')
Out[26]:
<Axes: title={'center': 'Absolute mean score for each facet (Sort by Wealth)'}>
No description has been provided for this image
In [27]:
# Normalised mean score for each facet (Sort by Wealth)

(df4[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean(axis = 0)).plot(kind = 'bar', title = 'Normalised mean score for each facet (Sort by Wealth)')
Out[27]:
<Axes: title={'center': 'Normalised mean score for each facet (Sort by Wealth)'}>
No description has been provided for this image
In [28]:
# Line Plot of Absolute Total Facet score vs sorted wealth

df4.plot(use_index = 'True' , kind = 'line', y = ['Total Score_19'], title = 'Total responses line plot sorted by Wealth',xlabel = 'Unique responses sorted by wealth', ylabel = 'Total Score_19')
Out[28]:
<Axes: title={'center': 'Total responses line plot sorted by Wealth'}, xlabel='Unique responses sorted by wealth', ylabel='Total Score_19'>
No description has been provided for this image
In [29]:
# Absolute Facet scores line plot from low to high wealth 
# I do understand that this is non readable

df4[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4']].plot( kind = 'line', title = 'Absolute Facet scores line plot', use_index = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[29]:
<matplotlib.legend.Legend at 0x2351d65f950>
No description has been provided for this image
In [30]:
# Trend line for Absolute Total Facet Score vs unique Q14. Wealth reponses

sns.regplot (data = df4, x = df4.index, y = 'Total Score_19')
Out[30]:
<Axes: ylabel='Total Score_19'>
No description has been provided for this image
In [31]:
# Trend line for Absolute Independent Facet Score vs Q14. Wealth reponses

fig1, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = df4.index, y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax1, label='Certainty score_5')
sns.regplot (data = df4, x = df4.index, y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax1, label='Uncertainty score_2')
sns.regplot (data = df4, x = df4.index, y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax1, label='Number Comprehension score_5')
sns.regplot (data = df4, x = df4.index, y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax1, label='Graph Comprehension score_3')
sns.regplot (data = df4, x = df4.index, y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax1, label='Calculation score_4' )

ax1.set(ylabel='Scores', xlabel='Q14. Wealth')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [32]:
# Count of number of responses under each quartile

df4.groupby('Quartile Number')[['Quartile Number']].count()
Out[32]:
Quartile Number
Quartile Number
1 122
2 74
3 89
4 95
In [33]:
df4.loc[df4['Quartile Number']==1]
Out[33]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_% Quartile Number
0 2305 4 0.0 1 29 2 1 0 3 2 2 8 20 0 60 66.666667 50 42.105263 1
1 5760 3 0.0 2 57 3 1 0 3 3 1 8 20 0 60 100.000000 25 42.105263 1
2 5463 6 0.0 1 30 2 2 1 4 3 2 12 40 50 80 100.000000 50 63.157895 1
3 796 2 0.0 1 35 2 5 2 4 2 3 16 100 100 80 66.666667 75 84.210526 1
4 2578 3 0.0 3 33 2 1 0 2 2 0 5 20 0 40 66.666667 0 26.315789 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
117 8449 2 5000.0 3 38 3 5 0 2 3 2 12 100 0 40 100.000000 50 63.157895 1
118 8979 2 5000.0 3 26 2 4 1 3 1 1 10 80 50 60 33.333333 25 52.631579 1
119 10476 4 5000.0 1 23 2 0 0 3 2 2 7 0 0 60 66.666667 50 36.842105 1
120 9214 3 5000.0 2 35 2 3 0 4 0 3 10 60 0 80 0.000000 75 52.631579 1
121 1952 1 5000.0 3 61 4 1 2 3 2 2 10 20 100 60 66.666667 50 52.631579 1

122 rows × 19 columns

In [34]:
# This graph is basically a line plot of the Total score_19 across first quartile unique responses which are ordered by income

df4.loc[df4['Quartile Number']==1].plot(use_index = 'True' , kind = 'line', y = ['Total Score_19'], title = 'Total Score line plot sorted by Wealth Q1',xlabel = 'Unique responses', ylabel = 'Total Score_19')
Out[34]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q1'}, xlabel='Unique responses', ylabel='Total Score_19'>
No description has been provided for this image
In [35]:
# Trend line for Absolute Total Facet Score vs unique Q14. Income reponses in Quartile 1

sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Total Score_19')
Out[35]:
<Axes: ylabel='Total Score_19'>
No description has been provided for this image
In [36]:
# Trend line for Absolute Independent Facet Score vs Q14. Wealth reponses in Quartile 1

fig1, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax2, label='Certainty score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax2, label='Uncertainty score_2')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax2, label='Number Comprehension score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax2, label='Graph Comprehension score_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax2, label='Calculation score_4' )

ax2.set(ylabel='Scores', xlabel='Q14. Wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [37]:
df4.loc[df4['Quartile Number']==2]
Out[37]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_% Quartile Number
122 2053 2 6000.0 3 23 2 5 0 0 2 2 9 100 0 0 66.666667 50 47.368421 2
123 1326 1 6200.0 1 49 3 4 1 3 2 1 11 80 50 60 66.666667 25 57.894737 2
124 10822 5 6666.0 2 28 2 3 0 3 0 0 6 60 0 60 0.000000 0 31.578947 2
125 6128 1 8000.0 1 25 2 5 2 4 2 3 16 100 100 80 66.666667 75 84.210526 2
126 13429 2 8000.0 2 24 2 5 0 3 2 2 12 100 0 60 66.666667 50 63.157895 2
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
191 11387 6 30000.0 2 37 3 3 0 4 2 3 12 60 0 80 66.666667 75 63.157895 2
192 13397 3 30000.0 1 28 2 1 1 4 2 2 10 20 50 80 66.666667 50 52.631579 2
193 6789 4 30000.0 1 52 3 3 2 4 2 3 14 60 100 80 66.666667 75 73.684211 2
194 391 2 30000.0 3 52 3 2 1 0 1 3 7 40 50 0 33.333333 75 36.842105 2
195 10277 6 30000.0 1 31 2 3 2 4 3 2 14 60 100 80 100.000000 50 73.684211 2

74 rows × 19 columns

In [38]:
# This graph is basically a line plot of the Total score_19 across second quartile unique responses which are ordered by income

df4.loc[df4['Quartile Number']==2].plot(use_index = 'True' , kind = 'line', y = ['Total Score_19'], title = 'Total Score line plot sorted by Wealth Q1',xlabel = 'Unique responses', ylabel = 'Total Score_19')
Out[38]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q1'}, xlabel='Unique responses', ylabel='Total Score_19'>
No description has been provided for this image
In [39]:
# Trend line for Absolute Total Facet Score vs unique Q14. Wealth reponses in Quartile 2

sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Total Score_19')
Out[39]:
<Axes: ylabel='Total Score_19'>
No description has been provided for this image
In [40]:
# Trend line for Absolute Independent Facet Score vs Q14. Wealth reponses in Quartile 2

fig1, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax3, label='Certainty score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax3, label='Uncertainty score_2')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax3, label='Number Comprehension score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax3, label='Graph Comprehension score_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax3, label='Calculation score_4' )

ax3.set(ylabel='Scores', xlabel='Q14. Wealth')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [41]:
df4.loc[df4['Quartile Number']==3]
Out[41]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_% Quartile Number
196 5909 3 32500.0 2 30 2 5 0 1 1 3 10 100 0 20 33.333333 75 52.631579 3
197 10855 4 34000.0 2 55 3 2 0 2 1 2 7 40 0 40 33.333333 50 36.842105 3
198 14112 3 34865.0 2 32 2 5 0 3 2 3 13 100 0 60 66.666667 75 68.421053 3
199 13314 5 35000.0 2 30 2 3 0 1 3 0 7 60 0 20 100.000000 0 36.842105 3
200 10801 3 35000.0 2 52 3 1 2 3 3 2 11 20 100 60 100.000000 50 57.894737 3
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
280 9549 4 160000.0 2 42 3 5 0 4 3 3 15 100 0 80 100.000000 75 78.947368 3
281 9194 3 160000.0 2 43 3 3 1 2 2 1 9 60 50 40 66.666667 25 47.368421 3
282 1013 5 175000.0 3 73 4 4 1 4 2 2 13 80 50 80 66.666667 50 68.421053 3
283 8166 6 175000.0 2 60 4 3 1 4 3 3 14 60 50 80 100.000000 75 73.684211 3
284 5837 4 180000.0 2 54 3 5 1 4 2 3 15 100 50 80 66.666667 75 78.947368 3

89 rows × 19 columns

In [42]:
# This graph is basically a line plot of the Total score_19 across third quartile unique responses which are ordered by wealth

df4.loc[df4['Quartile Number']==3].plot(use_index = 'True' , kind = 'line', y = ['Total Score_19'], title = 'Total Score line plot sorted by Wealth Q3',xlabel = 'Unique responses', ylabel = 'Total Score_19')
Out[42]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q3'}, xlabel='Unique responses', ylabel='Total Score_19'>
No description has been provided for this image
In [43]:
# Trend line for Absolute Total Facet Score vs unique Q14. Wealth reponses in Quartile 3

sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Total Score_19')
Out[43]:
<Axes: ylabel='Total Score_19'>
No description has been provided for this image
In [44]:
# Trend line for Absolute Independent Facet Score vs Q14. Wealth reponses in Quartile 3

fig1, ax4 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax4, label='Certainty score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax4, label='Uncertainty score_2')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax4, label='Number Comprehension score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax4, label='Graph Comprehension score_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax4, label='Calculation score_4' )

ax4.set(ylabel='Scores', xlabel='Q14. Wealth')
ax4.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [45]:
df4.loc[df4['Quartile Number']==4]
Out[45]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_% Quartile Number
285 11536 4 185000.0 2 51 3 2 1 2 0 1 6 40 50 40 0.000000 25 31.578947 4
286 1315 2 200000.0 2 57 3 1 2 4 2 3 12 20 100 80 66.666667 75 63.157895 4
287 490 1 200000.0 3 65 4 1 1 4 3 1 10 20 50 80 100.000000 25 52.631579 4
288 13403 5 200000.0 2 32 2 3 1 3 2 2 11 60 50 60 66.666667 50 57.894737 4
289 13350 6 200000.0 1 30 2 2 2 3 2 2 11 40 100 60 66.666667 50 57.894737 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
375 15 6 1250000.0 2 68 4 2 1 4 3 3 13 40 50 80 100.000000 75 68.421053 4
376 970 6 1350000.0 3 64 4 2 1 4 3 4 14 40 50 80 100.000000 100 73.684211 4
377 5592 7 1500000.0 2 64 4 3 1 5 3 3 15 60 50 100 100.000000 75 78.947368 4
378 10809 6 1650000.0 1 59 3 3 2 5 3 2 15 60 100 100 100.000000 50 78.947368 4
379 278 6 3500000.0 1 66 4 5 2 3 3 4 17 100 100 60 100.000000 100 89.473684 4

95 rows × 19 columns

In [46]:
# This graph is basically a line plot of the Total score_19 across fourth quartile unique responses which are ordered by Wealth

df4.loc[df4['Quartile Number']==4].plot(use_index = 'True' , kind = 'line', y = ['Total Score_19'], title = 'Total Score line plot sorted by Wealth Q4',xlabel = 'Unique responses', ylabel = 'Total Score_19')
Out[46]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q4'}, xlabel='Unique responses', ylabel='Total Score_19'>
No description has been provided for this image
In [47]:
# Trend line for Absolute Total Facet Score vs unique Q14. Wealth reponses in Quartile 4

sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Total Score_19')
Out[47]:
<Axes: ylabel='Total Score_19'>
No description has been provided for this image
In [48]:
# Trend line for Absolute Independent Facet Score vs Q14. Wealth reponses in Quartile 4

fig1, ax5 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax5, label='Certainty score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax5, label='Uncertainty score_2')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax5, label='Number Comprehension score_5')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax5, label='Graph Comprehension score_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax5, label='Calculation score_4' )

ax5.set(ylabel='Scores', xlabel='Q14. Wealth')
ax5.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [ ]:
In [49]:
# Absolute Total Facet scores mean line plot from low to high Wealth

df4.groupby('Quartile Number')[['Total Score_19']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[49]:
<matplotlib.legend.Legend at 0x2351f5c6c90>
No description has been provided for this image
In [50]:
# Absolute Facet scores mean line plot from low to high wealth

df4.groupby('Quartile Number')[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[50]:
<matplotlib.legend.Legend at 0x2351f32f550>
No description has been provided for this image
In [51]:
# Normalised Facet scores mean line plot from low to high wealth


df4.groupby('Quartile Number')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot from low to high wealth',  xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[51]:
<matplotlib.legend.Legend at 0x2351f63ac90>
No description has been provided for this image
In [52]:
# Normalised mean scores for each facet stacked

df4.groupby('Quartile Number')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[52]:
<matplotlib.legend.Legend at 0x2351fc8b290>
No description has been provided for this image
In [53]:
# Normalised mean scores for each facet for each wealth response Quartile

df4.groupby('Quartile Number')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[53]:
<matplotlib.legend.Legend at 0x23520358850>
No description has been provided for this image
In [54]:
# Trend line for Absolute Total Facet Score vs Quartiles

sns.regplot (data = df4, x = 'Quartile Number', y = 'Total Score_19')
Out[54]:
<Axes: xlabel='Quartile Number', ylabel='Total Score_19'>
No description has been provided for this image
In [55]:
# Trend line for Absolute Independent Facet Score vs Quartiles

fig2, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = 'Quartile Number', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax2, label='Certainty score_5')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax2, label='Uncertainty score_2')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax2, label='Number Comprehension score_5')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax2, label='Graph Comprehension score_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax2, label='Calculation score_4' )

ax2.set(ylabel='Scores', xlabel='Q14. Wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [56]:
# Violine Plot for Total Score_19 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Total Score_19')
Out[56]:
<Axes: xlabel='Quartile Number', ylabel='Total Score_19'>
No description has been provided for this image
In [57]:
# Violine Plot for Calculation score_4 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Calculation score_4')
Out[57]:
<Axes: xlabel='Quartile Number', ylabel='Calculation score_4'>
No description has been provided for this image
In [58]:
# Violine Plot for Graph Comprehension score_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Graph Comprehension score_3')
Out[58]:
<Axes: xlabel='Quartile Number', ylabel='Graph Comprehension score_3'>
No description has been provided for this image
In [59]:
# Violine Plot for Number Comprehension score_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Number Comprehension score_5')
Out[59]:
<Axes: xlabel='Quartile Number', ylabel='Number Comprehension score_5'>
No description has been provided for this image
In [60]:
# Violine Plot for Certainty score_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Certainty score_5')
Out[60]:
<Axes: xlabel='Quartile Number', ylabel='Certainty score_5'>
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [61]:
# Data Frame 5 or df5 is an aggregate data on facet total scores and over all total score, along with  education data vs each response. 
# This data set is now sorted by education.

df5 = df2.sort_values(by = 'isced')
df5 = df5.reset_index(drop = True)

df5
Out[61]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
0 6873 6 NaN 1 25 2 1 1 2 1 1 6 20 50 40 33.333333 25 31.578947
1 7875 5 NaN 1 63 4 1 1 3 2 2 9 20 50 60 66.666667 50 47.368421
2 7876 6 25000.0 1 41 3 3 2 4 3 3 15 60 100 80 100.000000 75 78.947368
3 7877 7 NaN 1 55 3 5 2 4 3 3 17 100 100 80 100.000000 75 89.473684
4 7884 6 950000.0 1 52 3 5 2 5 2 4 18 100 100 100 66.666667 100 94.736842
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
647 78 1 NaN 3 51 3 2 2 5 3 2 14 40 100 100 100.000000 50 73.684211
648 6130 1 NaN 3 32 2 2 0 2 1 0 5 40 0 40 33.333333 0 26.315789
649 6138 7 NaN 3 34 2 2 1 3 0 0 6 40 50 60 0.000000 0 31.578947
650 467 7 NaN 3 55 3 4 0 3 2 3 12 80 0 60 66.666667 75 63.157895
651 405 2 NaN 3 48 3 5 0 4 1 2 12 100 0 80 33.333333 50 63.157895

652 rows × 18 columns

In [62]:
# since we didnt drop any values, the dataset is essentially the same as df2, but sorted
In [63]:
# Mean of each facet as a % for each category of isced

df5.groupby('isced')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%']].mean()
Out[63]:
Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_%
isced
1 60.000000 47.727273 66.136364 66.035354 57.670455
2 55.462185 36.134454 60.000000 60.224090 46.008403
3 53.466667 41.333333 57.466667 58.222222 46.166667
In [64]:
# Count of responses for each category isced

df5.groupby('isced')[['responseid']].count()
Out[64]:
responseid
isced
1 264
2 238
3 150
In [65]:
# Absolute Total Facet scores mean line plot from low to high education

df5.groupby('isced')[['Total Score_19']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[65]:
<matplotlib.legend.Legend at 0x2351f17b290>
No description has been provided for this image
In [66]:
# Absolute Facet scores mean line plot from low to high edu

df5.groupby('isced')[['Certainty score_5','Uncertainty score_2','Number Comprehension score_5','Graph Comprehension score_3','Calculation score_4']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[66]:
<matplotlib.legend.Legend at 0x2352009aad0>
No description has been provided for this image
In [67]:
# Normalised Facet scores mean line plot from low to high edu


df5.groupby('isced')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[67]:
<matplotlib.legend.Legend at 0x23520122410>
No description has been provided for this image
In [68]:
# Normalised mean scores for each facet stacked

df5.groupby('isced')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[68]:
<matplotlib.legend.Legend at 0x23521ba8d90>
No description has been provided for this image
In [69]:
# Normalised mean scores for each facet for each edu response category

df5.groupby('isced')[['Certainty score_%','Uncertainty score_%','Number Comprehension score_%','Graph Comprehension score_%','Calculation score_%','Total Score_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each edu response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[69]:
<matplotlib.legend.Legend at 0x235217e3150>
No description has been provided for this image
In [70]:
# Trend line for Absolute Total Facet Score vs edu (isced) reponses

sns.regplot (data = df5, x = 'isced', y = 'Total Score_19')
Out[70]:
<Axes: xlabel='isced', ylabel='Total Score_19'>
No description has been provided for this image
In [71]:
# Trend line for Absolute Independent Facet Score vs edu (isced) reponses

fig, ax6 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df5, x = 'isced', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax6, label='Certainty score_5')
sns.regplot (data = df5, x = 'isced', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax6, label='Uncertainty score_2')
sns.regplot (data = df5, x = 'isced', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax6, label='Number Comprehension score_5')
sns.regplot (data = df5, x = 'isced', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax6, label='Graph Comprehension score_3')
sns.regplot (data = df5, x = 'isced', y = 'Calculation score_4',fit_reg=True, ci=None, ax=ax6, label='Calculation score_4' )

ax6.set(ylabel='Scores', xlabel='isced')
ax6.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [72]:
# Violine Plot for Total Score_19 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Total Score_19')
Out[72]:
<Axes: xlabel='isced', ylabel='Total Score_19'>
No description has been provided for this image
In [73]:
# Violine Plot for Total Score_19 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Calculation score_4')
Out[73]:
<Axes: xlabel='isced', ylabel='Calculation score_4'>
No description has been provided for this image
In [74]:
# Violine Plot for Graph Comprehension score_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Graph Comprehension score_3')
Out[74]:
<Axes: xlabel='isced', ylabel='Graph Comprehension score_3'>
No description has been provided for this image
In [75]:
# Violine Plot for Number Comprehension score_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Number Comprehension score_5')
Out[75]:
<Axes: xlabel='isced', ylabel='Number Comprehension score_5'>
No description has been provided for this image
In [76]:
# Violine Plot for Uncertainty score_2 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Uncertainty score_2')
Out[76]:
<Axes: xlabel='isced', ylabel='Uncertainty score_2'>
No description has been provided for this image
In [77]:
# Violine Plot for Certainty score_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Certainty score_5')
Out[77]:
<Axes: xlabel='isced', ylabel='Certainty score_5'>
No description has been provided for this image
In [78]:
# Descriptive stats for the data set, isced = 1
# NA values of wealth are removed

df6 = df5.dropna(axis = 0, subset = 'Q14. Wealth')
df6.drop(df6[df6['Q14. Wealth'] == 3500000].index, inplace = True)
df6.loc[df6['isced']==1][['Q14. Wealth']].describe()

C:\Users\SHIVAM\AppData\Local\Temp\ipykernel_11988\3962586432.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df6.drop(df6[df6['Q14. Wealth'] == 3500000].index, inplace = True)
Out[78]:
Q14. Wealth
count 1.690000e+02
mean 1.708374e+05
std 2.564941e+05
min 0.000000e+00
25% 5.000000e+03
50% 5.500000e+04
75% 2.500000e+05
max 1.650000e+06
In [79]:
# Descriptive stats for the data set, isced = 2

df6.loc[df6['isced']==2][['Q14. Wealth']].describe()
Out[79]:
Q14. Wealth
count 1.360000e+02
mean 1.387606e+05
std 2.632906e+05
min 0.000000e+00
25% 5.000000e+03
50% 3.000000e+04
75% 1.600000e+05
max 1.500000e+06
In [80]:
# Descriptive stats for the data set, isced = 3

df6.loc[df6['isced']==3][['Q14. Wealth']].describe()
Out[80]:
Q14. Wealth
count 7.400000e+01
mean 9.383407e+04
std 2.127450e+05
min 0.000000e+00
25% 1.000000e+03
50% 1.000000e+04
75% 4.875000e+04
max 1.350000e+06
In [81]:
# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='Wealth')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [82]:
# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='Wealth')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [83]:
# Trend line for Absolute Certainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty score_5', xlabel='Wealth')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [84]:
# Trend line for Absolute Uncertainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='Uncertainty score_2', xlabel='Wealth')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [85]:
# Trend line for Absolute Number Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Number Comprehension score_5', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [86]:
# Trend line for Absolute Graph Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Graph Comprehension score_3', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [87]:
# Trend line for Absolute Calculation Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['Q14. Wealth'], y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['Q14. Wealth'], y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['Q14. Wealth'], y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Calculation score_4', xlabel='Wealth')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [88]:
# Descriptive stats for the data set, isced = 1
# NA values of income are removed

df7 = df5
df7.drop(df7[df7['Q12. Income'] == 7].index, inplace = True)

df7.loc[df7['isced']==1][['Q12. Income']].describe()
Out[88]:
Q12. Income
count 240.000000
mean 3.704167
std 1.628712
min 1.000000
25% 2.000000
50% 4.000000
75% 5.000000
max 6.000000
In [89]:
df7.loc[df7['isced']==2][['Q12. Income']].describe()
Out[89]:
Q12. Income
count 211.000000
mean 3.175355
std 1.398148
min 1.000000
25% 2.000000
50% 3.000000
75% 4.000000
max 6.000000
In [90]:
df7.loc[df7['isced']==3][['Q12. Income']].describe()
Out[90]:
Q12. Income
count 126.000000
mean 2.619048
std 1.563878
min 1.000000
25% 1.000000
50% 2.000000
75% 3.000000
max 6.000000
In [91]:
# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[df7['isced']==1]['Total Score_19'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[df7['isced']==2]['Total Score_19'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[df7['isced']==3]['Total Score_19'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='INCOME')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [92]:
# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Total Score_19', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='INCOME')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [93]:
# Trend line for Absolute Certainty Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Certainty score_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty score_5', xlabel='INCOME')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [94]:
# Trend line for Absolute Uncertainty Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Uncertainty score_2', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='Uncertainty score_2', xlabel='INCOME')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [95]:
# Trend line for Absolute Number Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Number Comprehension score_5', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Number Comprehension score_5', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [96]:
# Trend line for Absolute Graph Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Graph Comprehension score_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Graph Comprehension score_3', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [97]:
# Trend line for Absolute Calculation Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'Q12. Income', y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'Q12. Income', y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'Q12. Income', y = 'Calculation score_4', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Calculation score_4', xlabel='INCOME')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [98]:
df7
Out[98]:
responseid Q12. Income Q14. Wealth isced age age_rec Certainty score_5 Uncertainty score_2 Number Comprehension score_5 Graph Comprehension score_3 Calculation score_4 Total Score_19 Certainty score_% Uncertainty score_% Number Comprehension score_% Graph Comprehension score_% Calculation score_% Total Score_%
0 6873 6 NaN 1 25 2 1 1 2 1 1 6 20 50 40 33.333333 25 31.578947
1 7875 5 NaN 1 63 4 1 1 3 2 2 9 20 50 60 66.666667 50 47.368421
2 7876 6 25000.0 1 41 3 3 2 4 3 3 15 60 100 80 100.000000 75 78.947368
4 7884 6 950000.0 1 52 3 5 2 5 2 4 18 100 100 100 66.666667 100 94.736842
5 8688 3 155000.0 1 50 3 4 0 4 1 1 10 80 0 80 33.333333 25 52.631579
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
645 115 2 100000.0 3 49 3 2 1 3 1 2 9 40 50 60 33.333333 50 47.368421
646 104 1 1000.0 3 67 4 1 0 3 0 1 5 20 0 60 0.000000 25 26.315789
647 78 1 NaN 3 51 3 2 2 5 3 2 14 40 100 100 100.000000 50 73.684211
648 6130 1 NaN 3 32 2 2 0 2 1 0 5 40 0 40 33.333333 0 26.315789
651 405 2 NaN 3 48 3 5 0 4 1 2 12 100 0 80 33.333333 50 63.157895

577 rows × 18 columns

In [99]:
# Since we already have a classification for Age groups in the form of age_rec, we will use that.
# We will also use median of age to see if it yields any relevant results, as instructed.

# AXES to be used = Age or age groups, ISCED, Income

# Age groups = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 59 y/o
# Age group 4 = 60 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)
In [100]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_19', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [101]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_19', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [102]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty score_5', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty score_5', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Certainty score_5', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [103]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Uncertainty score_2', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Uncertainty score_2', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Uncertainty score_2', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [104]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Number Comprehension score_5', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Number Comprehension score_5', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Number Comprehension score_5', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [105]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Graph Comprehension score_3', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Graph Comprehension score_3', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Graph Comprehension score_3', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [106]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Calculation score_4', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Calculation score_4', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Calculation score_4', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
No description has been provided for this image
In [107]:
# Age median method
# Age_median
age_m = df7['age'].mean()
age_m
Out[107]:
45.98786828422877
In [108]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [109]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Total Score_19'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [110]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty score_5', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Certainty score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty score_5', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [111]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Uncertainty score_2', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Uncertainty score_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Uncertainty score_2', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [112]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Number Comprehension score_5', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Number Comprehension score_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Number Comprehension score_5', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [113]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Graph Comprehension score_3', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Graph Comprehension score_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Graph Comprehension score_3', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [114]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Calculation score_4', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'Q12. Income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Calculation score_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Calculation score_4', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [115]:
# 3D graph representation

fig = plt.figure(figsize = (6,6))
 
ax = fig.add_subplot(111, projection='3d')
 
# defining axes
z = df7.loc[:,'age'] # Age on Z axis
x = df7.loc[:,'Q12. Income'] # Income on X axis
y = df7.loc[:,'isced'] # ISCED on Y axis
ax.scatter(x, y, z)
 
ax.set_title('3d Scatter plot')
ax.set(ylabel='ISCED', xlabel='Income', zlabel = 'Age')

plt.show()
No description has been provided for this image
In [116]:
fig = plt.figure(figsize = (6,6))
ax = fig.add_subplot(111, projection='3d')

z = df7.loc[:,'age'] # Age on Z axis
x = df7.loc[:,'Q12. Income'] # Income on X axis
y = df7.loc[:,'isced'] # ISCED on Y axis
c = df7.loc[:,'Total Score_19'] # on Heat scale

img = ax.scatter(x, y, z, c=c, cmap=plt.hot())
ax.set(ylabel='ISCED', xlabel='Income', zlabel = 'Age')
ax.set_title('3d Scatter plot')

fig.colorbar(img, anchor = (2.0,2.0), label = 'Total Score_19')
plt.show()
No description has been provided for this image
In [117]:
fig = plt.figure(figsize = (6,6))
ax = fig.add_subplot(111, projection='3d')

c = df7.loc[:,'age'] # Age on Heat scale
x = df7.loc[:,'Q12. Income'] # Income on X axis
z = df7.loc[:,'isced'] # ISCED on Z axis
y = df7.loc[:,'Total Score_19'] # on Y axis

img = ax.scatter(x, y, z, c=c, cmap=plt.hot())
ax.set(ylabel='Total Score_19', xlabel='Income', zlabel = 'ISCED')
ax.set_title('3d Scatter plot')

fig.colorbar(img, anchor = (2.0,2.0), label = 'Age')
plt.show()
No description has been provided for this image
In [118]:
# 3D plots are amounting to be nigh unreadable, and usually speaking, the methods that we have used above are far more easier to visualise.
# However, I have included them just in case you are able to interpret them in your expertise.
In [ ]:
In [ ]:
In [ ]: